Traffic Simulation and Data

A single source of information for researchers and professionals, Traffic Simulation and Data: Validation Methods and Applications offers a complete overview of traffic data collection, state estimation, calibration and validation for traffic modelling and simulation. It derives from the Multitude Project—a European Cost Action project that incorporates work packages defining traffic simulation practice and research; highway and network modeling; and synthesis, dissemination, and training. This book addresses the calibration and validation of traffic models, and introduces necessary frameworks and techniques. It also includes viable methods for sensitivity analyses, and incorporates relevant tools for application. The book begins with a brief summary of various data collection techniques that can be applied to collect different data types. It then showcases various data processing and enhancement techniques for improving the quality of collected data. It also introduces the techniques according to the type of estimation, for example microscopic data enhancement, traffic state estimation, feature extraction and parameter identification techniques, and origin–destination matrix estimation. The material discusses the measures of performance, data error and goodness of fit, and optimization algorithms. It also contains the sensitivity analyses of parameters in traffic models. Describes the various tasks of calibration and validation Considers the best use of available data Presents the sensitivity analysis method Discusses typical issues of data error in transportation system data and how these errors can impact simulation results Details various methodologies for data collection, sensitivity analysis, calibration, and validation Examines benefits that result from the application of these methods Traffic Simulation and Data: Validation Methods and Applications serves as a key resource for transport engineers and planners, researchers, and graduate students in transport engineering and planning.